A potential field shared control approach for wheelchair navigation via brain-computer interface

Objective. Electroencephalography (EEG)-based brain-computer interfaces (BCIs) can help patients with disabilities control external devices directly without peripheral pathways. Due to the limitations in EEG signal quality, the performance of EEG-based BCIs may not be satisfactory. Shared control has become an important research direction in the field of brain-controlled wheelchairs (BCWs). However, most existing studies do not achieve the flexible movement of BCW in environments with narrow spaces. This study proposes a shared controller based on the potential field method to integrate environmental information and user commands intelligently.Approach. Considering the flexibility of wheelchair movement, we incorporated EEG decoding results obtained through the motor imagery paradigm and fused them with environmental information to create a fusion field. We then used these components separately to construct the BCI and obstacle fields. Twelve subjects participated in the virtual wheelchair navigation experiment, while five subjects took part in the real-world wheelchair navigation experiment, aiming to evaluate the control performance in different scenarios under three control modes (keyboard, BCI-only, and shared control).Main results. The experimental results show that the proposed shared controller: 1) significantly enhances navigation performance in both general and narrow environments compared with BCI-only control; 2) improves the total success rate from 8.33% to 83.33% in virtual complex environments and from 23.33% to 66.67% in real-world two-way navigation; 3) achieves success rates that are statistically comparable to keyboard control (p> 0.05). Moreover, the shared control reduced the average navigation time by nearly 100 s compared with BCI-only control in real-world experiments.Significance. This new shared control method improves the ability of BCWs to move flexibly in challenging, narrow environments.